系列文章目录

MNN createFromBuffer（一）
MNN createRuntime（二）
MNN createSession 之 Schedule（三）
MNN createSession 之创建流水线后端（四）
MNN Session::resize 之流水线编码（五）
MNN Session 创建执行器（六）

1、createSession

    依据 ScheduleConfig 和 RuntimeInfo 创建会话。

// source/core/Interpreter.cpp
Session* Interpreter::createSession(const ScheduleConfig& config, const RuntimeInfo& runtime) {return createMultiPathSession({config}, runtime);
}

1.1 createMultiPathSession

// source/core/Interpreter.cpp
Session* Interpreter::createMultiPathSession(const std::vector<ScheduleConfig>& configs, const RuntimeInfo& runtime) {// ...auto newSession =std::unique_ptr<Session>(new Session(std::move(info), mNet->modes, std::move(rt)));if (!newSession->valid()) {MNN_PRINT("Invalide Session!!\n");return nullptr;}auto result = newSession.get();auto validForResize = info.validForResize;if (validForResize && mNet->modes.inputMode == Session_Input_Inside && mNet->modes.resizeMode == Session_Resize_Direct) {result->resize();}if ((!mNet->cacheFile.empty()) && (!valid) && mNet->modes.backendMode == Session_Backend_Fix) {// Try to save extra cacheauto buffer = result->getCache();if (buffer.first != nullptr && buffer.second > 0) {MNN_PRINT("Write cache to %s, size = %zu\n", mNet->cacheFile.c_str(), buffer.second);writeCacheFile(mNet, buffer);mNet->lastCacheSize = buffer.second;// Write CachecacheMode = cacheMode | 2;}}// Reset cacheresult->loadCache(nullptr, 0);mNet->sessions.emplace_back(std::move(newSession));#ifdef MNN_INTERNAL_ENABLEDint precision = BackendConfig::Precision_Normal;if (nullptr != configs[0].backendConfig) {precision = configs[0].backendConfig->precision;}int mode = configs[0].mode;mNet->sessionInfo.insert(std::make_pair(result, std::make_tuple(precision, mode)));if (shouldLog(FREQ_HIGH)) {std::map<std::string, std::string> metrics = mNet->basicLogginData;metrics.emplace("UUID", mNet->uuid);metrics.emplace("Time", std::to_string((float)_timer.durationInUs() / 1024.0f));metrics.emplace("Backend", std::to_string(configs[0].type));metrics.emplace("Precision", std::to_string(precision));metrics.emplace("Mode", std::to_string(mode));metrics.emplace("Cache", std::to_string(cacheMode));metrics.emplace("CacheSize", std::to_string((float)(mNet->lastCacheSize / 1024.0f)));metrics.emplace("ModelSize", std::to_string ((float)mNet->buffer.size() / 1024.0f / 1024.0f));metrics.emplace("Usage", std::to_string((int) mNet->net->usage()));metrics.emplace("API", "Interpreter::createMultiPathSession");logAsync(metrics);}
#endif // MNN_INTERNAL_ENABLEDreturn result;
}

1.1.1 Session 类 ModeGroup

// source/core/Session.hpp
class MNN_PUBLIC Session {
public:struct ModeGroup {Interpreter::SessionMode callBackMode = Interpreter::Session_Debug;Interpreter::SessionMode inputMode = Interpreter::Session_Input_Inside;Interpreter::SessionMode outputMode = Interpreter::Session_Output_Inside;Interpreter::SessionMode backendMode = Interpreter::Session_Backend_Fix;Interpreter::SessionMode resizeMode = Interpreter::Session_Resize_Direct;Interpreter::SessionMode memoryUsageMode = Interpreter::Session_Memory_Collect;Interpreter::SessionMode codegenMode = Interpreter::Session_Codegen_Disable;int memoryAllocatorType = 0;int maxTuningNumber = MNN_DEFAULT_TUNING_NUMBER;};Session(Schedule::ScheduleInfo&& info, const ModeGroup& mode,RuntimeInfo&& runtime);~Session();Session* clone(RuntimeInfo&& runtime, std::shared_ptr<Schedule::ScheduleInfo> sharedConst);
public:/*** @brief infer.* @return result code.*/ErrorCode run() const;/*** @brief infer with callbacks and sync option.* @param enterCallback callback before each op.* @param exitCallback  callback after each op.* @param sync          wait until all ops done before return or not.* @return result code.*/ErrorCode runWithCallBack(const TensorCallBackWithInfo& enterCallback, const TensorCallBackWithInfo& exitCallback,bool sync = false) const;bool getInfo(Interpreter::SessionInfoCode code, void* ptr) const;public:/*** @brief resize tensors and buffers responding to input changes.* @return result code.*/ErrorCode resize();/*** @brief set if needs resize.* @param flag  needs resize or not.*/void setNeedResize(bool flag = true) {mNeedResize = flag;}void setNeedMalloc(bool flag = true) {mNeedMalloc = flag;}Runtime* getCPURuntime() {return mRuntime.second.get();}public:/*** @brief get backend that create the tensor.* @param tensor    given tensor.* @return backend that create the tensor, NULL if the tensor is created by default backend (CPU backend).*/const Backend* getBackEnd(const Tensor* tensor) const;/*** @brief get input tensor for given op name.* @param name given op name. if NULL, return first input tensor.* @return input tensor if found, NULL otherwise.*/Tensor* getInput(const char* name) const;/*** @brief get output tensor for given op name.* @param name given op name. if NULL, return first output tensor.* @return output tensor if found, NULL otherwise.*/Tensor* getOutput(const char* name) const;/*** @brief get output tensors map.* @return get output tensors map.*/const std::map<std::string, Tensor*>& getOutputAll() const;const std::map<std::string, Tensor*>& getInputAll() const;/*** @brief check session is valid or not.* @return session is valid or not.*/inline bool valid() const {return mValid;}/*** @brief update the session's const value to origin model's const blob.* @return errorcode*/ErrorCode updateToModel(Net* net) const;void waitAsyncResize();bool hasAsyncWork();bool loadCache(const void* buffer, size_t size);std::pair<const void*, size_t> getCache();Tensor* getTensor(int index) const;Schedule::PipelineInfo& getPipelineInfo(int index) const;
protected:const std::vector<std::shared_ptr<Pipeline>>& getPipelines() const {return this->mPipelines;}private:void _clearCache();void _setUpTensorInfo(const Schedule::ScheduleInfo& info);private:RuntimeInfo mRuntime;std::vector<std::shared_ptr<Pipeline>> mPipelines;bool mNeedResize = true;bool mValid      = true;bool mNeedMalloc = true;Interpreter::SessionMode mCallBackMode;Interpreter::SessionMode mMemoryUsageMode;Interpreter::SessionMode mCodegenMode;Schedule::ScheduleInfo mInfo;ModeGroup mMode;
};

1.1.2 Session::Session

// source/core/Session.cpp
Session::Session(Schedule::ScheduleInfo&& info, const ModeGroup& mode, RuntimeInfo&& runtime) {mMode = mode;mRuntime = std::move(runtime);if (info.pipelineInfo.empty()) {mValid = false;return;}mInfo = std::move(info);for (auto& iter : mInfo.pipelineInfo) {_createPipelineBackend(iter, mRuntime);Pipeline::TuningAttr attr;attr.maxTuningNumber = mode.maxTuningNumber;attr.autoSetOpType = mode.backendMode == Interpreter::Session_Backend_Auto;auto rt    = mRuntime.first.find(iter.first.info.type)->second.get();auto cpuRuntime = mRuntime.second;std::shared_ptr<Pipeline> newPipeline(new Pipeline(std::move(iter), mode.inputMode == Interpreter::Session_Input_Inside, mode.outputMode == Interpreter::Session_Output_User, attr, rt, cpuRuntime.get()));mPipelines.emplace_back(std::move(newPipeline));}mCallBackMode = mode.callBackMode;mMemoryUsageMode = mode.memoryUsageMode;mCodegenMode = mode.codegenMode;
}

1.1.2.1 _createPipelineBackend

    创建流水线后端。BackendCache

// source/core/Session.cpp
// typedef std::pair<BackendCache, std::vector<OpCacheInfo>> PipelineInfo;
//
//   struct BackendCache {
//      Backend::Info info;
//      BackendConfig config;
//      std::pair<std::shared_ptr<Backend>, std::shared_ptr<Backend>> cache;
//      bool needComputeShape = true;
//      bool needComputeGeometry = true;
//      bool reportError = true;
//      std::map<Tensor*, TENSORCACHE> inputTensorCopyCache;
//  };
//
// typedef std::pair< std::map<MNNForwardType, std::shared_ptr<Runtime>>,  \
//						std::shared_ptr<Runtime>> RuntimeInfo;
//
static void _createPipelineBackend(Schedule::PipelineInfo& iter, RuntimeInfo& runtime) {// iter.first 类型为 struct BackendCache if (iter.first.cache.first != nullptr) {return;}// runtime.first 类型为 std::map<MNNForwardType, std::shared_ptr<Runtime>>// 根据 MNNForwardType（MNN_FORWARD_VULKAN） 获取对应的 Runtime（VulkanRuntime）auto rt    = runtime.first.find(iter.first.info.type)->second.get();// runtime.second 为默认 Runtime（CPURuntime）auto cpuRuntime = runtime.second;bool specialUsage = false;if (iter.first.info.user != nullptr) {specialUsage = iter.first.info.user->flags > 0;}// 此处运行 VulkanRuntime::onCreate，创建对应的 Backend（VulkanBackend）// iter.first.cache 类型为 std::pair<std::shared_ptr<Backend>, std::shared_ptr<Backend>>iter.first.cache.first.reset(rt->onCreate(iter.first.info.user));std::shared_ptr<Backend> second;if (iter.first.cache.first->type() == MNN_FORWARD_CPU && (!specialUsage)) {iter.first.cache.second = iter.first.cache.first;} else {// Const Backend shouldn't be used as default backend// The session may be schedule multi-thread but const backend is the same// We need create a new backend to do size compute / not support op compute// 创建默认的 Backend（CPUBackend）BackendConfig defaultConfig;defaultConfig.flags = 4;iter.first.cache.second.reset(cpuRuntime->onCreate(&defaultConfig));}
}

1.1.2.1.1 VulkanRuntime::onCreate

// source/backend/vulkan/runtime/VulkanRuntime.cpp
Backend* VulkanRuntime::onCreate(const BackendConfig* config) const {// FIXME: Use configreturn new VulkanBackend(this, mInfo);
}

1.1.2.1.1.1 VulkanBackend::VulkanBackend

// source/backend/vulkan/image/backend/VulkanBackend.cpp
VulkanBackend::VulkanBackend(const VulkanRuntime* runtime, const Backend::Info& info) : Backend(MNN_FORWARD_VULKAN) {mRuntime = runtime;mDirect = Backend::Info::INDIRECT != info.mode;mDynamicMemoryPool.reset(new VulkanMemoryPool(runtime->mMemoryPool.get()));auto& dev              = device();mFence                 = std::make_shared<VulkanFence>(dev);if (!mDirect) {mCmdBuffer.reset(runtime->mCmdPool->allocBuffer());}mInitBuffer.reset(runtime->mCmdPool->allocBuffer());
}

1.1.2.1.2 CPURuntime::onCreate

// source/backend/cpu/CPUBackend.cpp
Backend* CPURuntime::onCreate(const BackendConfig* config) const {auto precision = mPrecision;auto memory = mMemory;size_t flags = mFlags;if (nullptr != config) {precision = config->precision;flags = config->flags;memory = config->memory;}
#ifdef LOG_VERBOSEMNN_PRINT("cpu backend was created by runtime:%p\n", this);
#endif#ifdef MNN_USE_ARMV82auto core = MNNGetCoreFunctions();if (core->supportFp16arith && precision == BackendConfig::Precision_Low) {return new Arm82Backend(this, memory);}
#endif
#ifdef MNN_SUPPORT_BF16if (precision == BackendConfig::Precision_Low_BF16 && BF16Functions::get()) {return new BF16Backend(this);}
#endifif (flags == MNN_CPU_USE_DEFAULT_BACKEND) {return new CPUBackend(this, precision, memory, MNN_FORWARD_CPU, 0);}
#ifdef MNN_USE_SSEif (AVX2Backend::isValid()) {return new AVX2Backend(this, memory, flags);}
#endifreturn new CPUBackend(this, precision, memory, MNN_FORWARD_CPU, flags);
}

1.1.2.1.2.1 CPUBackend::CPUBackend

// source/backend/cpu/CPUBackend.cpp
CPUBackend::CPUBackend(const CPURuntime* runtime, BackendConfig::PrecisionMode precision, BackendConfig::MemoryMode memory, MNNForwardType type, size_t flags) : Backend(type) {
#ifdef LOG_VERBOSEMNN_PRINT("cpu backend create\n");
#endifmMemory = memory;mRuntime = const_cast<CPURuntime*>(runtime);std::shared_ptr<BufferAllocator::Allocator> defaultAlloc(BufferAllocator::Allocator::createRecurse(runtime->mStaticAllocator.get()));if (mRuntime->getAllocatorType() == Runtime::Allocator_Defer) {mDynamicAllocator.reset(new DeferBufferAllocator(defaultAlloc));} else {mDynamicAllocator.reset(new EagerBufferAllocator(defaultAlloc));}mStaticAllocator = runtime->mStaticAllocator;mPrecisionMode = precision;mCoreFunctions = MNNGetCoreFunctions();mInt8CoreFunctions = MNNGetInt8CoreFunctions();mCache = new CPUResizeCache;
}

1.1.2.2 Pipeline 类 TuningAttr、UnitInfo

// source/core/Pipeline.hpp
/** pipeline. one session may contains multiple pipeline, and one pipeline may contains more than one unit. */
class Pipeline : public NonCopyable {
public:struct TuningAttr {bool autoSetOpType;int maxTuningNumber;};Pipeline(Schedule::PipelineInfo&& info, bool allocInput, bool outputStatic, const TuningAttr& tune, const Runtime* rt, const Runtime* cpuRt);~Pipeline();class UnitInfo : public OperatorInfo {public:UnitInfo()          = default;virtual ~UnitInfo() = default;void setUp(const Command& cmd, int index, const Op* originOp, int totalIndex);};
public:/** encode :1. compute shape for every op's inputs and outputs;2. geometry transform;3. copy op, inputs and outputs tensor info to mBufferstatic_model:  3; dynamic_model: 1,2,3*/ErrorCode encode(bool supportDebug = false, bool permitCodegen = false);/** allocMemory: create Execution and alloc memory for every op */ErrorCode allocMemory(bool firstMalloc, bool permitCodegen);/** execute this pipline */ErrorCode execute();ErrorCode executeCallBack(const TensorCallBackWithInfo& before, const TensorCallBackWithInfo& after);Schedule::PipelineInfo& getPipelineInfo() {return mInfo;}float flops() const {return mFlops;}friend class Session;MNNForwardType getMainForwardType() const  {return mInfo.first.cache.first->type();}
private:void _copyInputs();void _pushTuningTask(std::vector<Schedule::OpCacheInfo>&& initInfos);void _recycleDynamicMemory(Command* command);Schedule::PipelineInfo mInfo;bool mAllocInput;bool mOutputStatic;TuningAttr mTuneAttr;float mFlops = 0.0f;bool mIsQuantModel = false;// For gpu or other backendstd::map<Tensor*, std::shared_ptr<Tensor>> mCacheConstTensors;std::map<Tensor*, std::shared_ptr<Tensor>> mShapeFixConstCache;
#ifndef MNN_BUILD_MINIGeometryComputer::Context mContext;Runtime::CompilerType mUseGeometry;
#endifconst Runtime* mRuntime;const Runtime* mCpuRuntime;
};

1.1.2.3 Pipeline::Pipeline

OpCacheInfo

// source/core/Pipeline.cpp
// typedef std::pair<BackendCache, std::vector<OpCacheInfo>> PipelineInfo;
//
//    /** pipeline info */
//    struct OpCacheInfo {
//        /** op */
//        const Op* op;
//        /** input tensors */
//        std::vector<Tensor*> inputs;
//        /** output tensors */
//        std::vector<Tensor*> outputs;
//        /** schedule type*/
//        Schedule::Type type = Schedule::Type::SEPARATE;
//
//        /**Command buffer for cache*/
//        CommandBuffer cacheBuffer;
//
//        /**Command buffer for execute*/
//        CommandBuffer executeBuffer;
//        
//        std::map<const Op*, std::shared_ptr<Execution>> executionCache;
//    };
//
Pipeline::Pipeline(Schedule::PipelineInfo&& info, bool allocInput, bool outputStatic, const TuningAttr& tune, const Runtime* rt, const Runtime* cpuRt)
#ifndef MNN_BUILD_MINI// mContext 类型为 GeometryComputer::Context: mContext(info.first.cache.second, info.first.cache.first->type(), info.first.info.user ? info.first.info.user->precision :  BackendConfig::Precision_Normal), mUseGeometry(rt->onGetCompilerType()) {
#else
{
#endifrt->onCheckInfo(info.first.info);mRuntime = rt;mCpuRuntime = cpuRt;mTuneAttr = tune;mAllocInput    = allocInput;mOutputStatic  = outputStatic;mInfo          = std::move(info);mIsQuantModel = false;// mInfo.second 类型为 std::vector<OpCacheInfo>for (auto& iter : mInfo.second) {for (auto t : iter.outputs) {if (TensorUtils::getDescribe(t)->quantAttr.get() != nullptr) {// 是否是量化模型mIsQuantModel = true;break;}}for (auto t : iter.inputs) {if (TensorUtils::getDescribe(t)->quantAttr.get() != nullptr) {mIsQuantModel = true;break;}}if (mIsQuantModel) {break;}}}

1.1.2.3.1 GeometryComputer::Context

class GeometryComputer {
public:virtual ~GeometryComputer() {// Do nothing}class MNN_PUBLIC Context {public:Context(std::shared_ptr<Backend> allocBackend, MNNForwardType type = MNN_FORWARD_CPU, BackendConfig::PrecisionMode precision = BackendConfig::Precision_Normal);~Context();void clear();void setBackend(Backend* backend);void getRasterCacheCreateRecursive(Tensor* src, CommandBuffer& cmd);// If has cache, return. Otherwise create cacheconst std::vector<std::shared_ptr<Tensor>>& searchConst(const Op* op);std::shared_ptr<Tensor> allocConst(const Op* key, const std::vector<int>& shape, halide_type_t type,Tensor::DimensionType dimType = Tensor::TENSORFLOW);bool allocTensor(Tensor* tenosr);inline MNNForwardType forwardType() const {return mForwardType;}inline BackendConfig::PrecisionMode precisionType() const {return mPrecision;}void pushCache(const CommandBuffer& buffer);std::shared_ptr<BufferStorage> mRasterOp;private:void getRasterCacheCreate(Tensor* src, CommandBuffer& cmd);std::map<const Op*, std::vector<std::shared_ptr<Tensor>>> mConstTensors;std::vector<std::shared_ptr<Tensor>> mEmpty;std::vector<std::shared_ptr<Tensor>> mTempConstTensors;std::shared_ptr<Backend> mBackend;MNNForwardType mForwardType;BackendConfig::PrecisionMode mPrecision;std::vector<SharedPtr<Command>> mRasterCmdCache;};static void init();MNN_PUBLIC static const GeometryComputer* search(int opType, Runtime::CompilerType compType);static void registerGeometryComputer(std::shared_ptr<GeometryComputer> comp, std::vector<int> type, Runtime::CompilerType compType = Runtime::Compiler_Geometry);virtual bool onCompute(const Op* op, const std::vector<Tensor*>& inputs, const std::vector<Tensor*>& outputs,Context& context, CommandBuffer& cmd) const = 0;virtual bool onRecompute(const Op* op, const std::vector<Tensor*>& inputs, const std::vector<Tensor*>& outputs,Context& context, CommandBuffer& cmd) const {return false;}
};

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